2014 CSE Abstracts

Abstract:Quality of experience (QoE) of a user
is one of the main factors which determine the reputation of cellular network
operators. In addition to the performance of cellular network nodes, the
hardware and software performance of different device types and applications
also largely define a user's QoE over the cellular network. This project deals
with the problem of maintaining QoE of cellular network users by proactive
detection of service degradation issues. Although, cellular network providers
utilize existing end-to-end service quality management systems for detecting
issues inside the network, but under certain conditions issues affecting QoE of
a group of customers may go undetected. These conditions may arise due to
problems in different dimensions such as mobile devices, applications, websites
and network nodes. We analyze aggregated TCP flow data across different geographical
regions over a period of six weeks to develop a regression based model for
estimating the network performance perceived by groups of users. Our analysis
show that specific user groups experience significantly different performance
behavior as compared to performance perceived by most of the users because of
their association with a particular device type and/or an application and/or a
website and/or a network node. We design a holistic performance monitoring
system to detect and localize issues, causing service degradation for groups of
customers sharing one or more of the above mentioned dimensions. Through a
recursive rule mining approach we show that not only the overall training error
is reduced but also there is a significant decrease in false positives.

Abstract:Today wireless communications suffer
from high transmission error especially in high data rates. It was believed
that channel condition is responsible for most of these errors but recent
research have proved that there are patterns in the bit error rate which are
not caused by channel conditions. It turns out that the bit error pattern has a
fluctuating nature which can potentially be used to improve the accuracy and
throughput of the system, in applications such as video streaming.

Our research has validated
the existence of such a pattern in different environments and across different
devices. Further, we have demonstrated that this behavior depends mainly on the
transmission rate. It appears that the pattern is caused by the decoder in the
receiver. Unfortunately the decoder codes are not available to public so we
cannot find what part of the algorithm is causing this behavior.

One major advantage of the
bit error pattern is that it is known prior to the transmission, so no
handshake or synchronization is required in order to use it. To utilize our
findings, we formulated the pattern and used it in video streaming. Our
preliminary results from simulations show more than 15% improvement in
throughput by utilizing the pattern. We are expecting more improvement in next
set of experiments.

Abstract:One of the critical factors prior to
deployment of any large scale biometric system is to have a realistic estimate
of its matching performance. In practice, evaluations are conducted on the
operational data to set an appropriate threshold on match scores before the
actual deployment. These performance estimates, though, are restricted by the
amount of available test data. To overcome this limitation, use of a large
number of 2D synthetic fingerprints for evaluating fingerprint systems had been
proposed. However, the utility of 2D synthetic fingerprints is limited in the
context of testing end-to-end fingerprint systems which involve the entire
matching process, from image acquisition to feature extraction and matching.
For a comprehensive

evaluation of fingerprint
systems, we propose creating 3D fingerprint phantoms (phantoms or imaging
phantoms are specially designed objects with known properties scanned or imaged
to evaluate, analyze, and tune the performance of various imaging devices) with
known characteristics (e.g., type, singular points and minutiae) by (i)
projecting 2D synthetic fingerprints with known characteristics onto a generic
3D finger surface and (ii)

printing the 3D fingerprint
phantoms using a commodity 3D printer. Experimental results show that the
captured images of the 3D fingerprint phantom using state-of-the-art
fingerprint sensors can be successfully matched to the 2D synthetic fingerprint
images (from which the phantom was generated) using a commercial fingerprint
matcher. This demonstrates that our method preserves the ridges and valleys
during the 3D fingerprint phantom creation process ensuring that the
synthesized 3D phantoms can be utilized for comprehensive evaluations of
fingerprint systems.

This work was supported in
part by National Institute of Standards and Technology (NIST)

Face Recognition: Identifying A Person Of Interest

Authors: Lacey Best-Rowden; Hu Han; Charles Otto; Anil K. Jain

Abstract:As face recognition applications
progress from constrained and controlled scenarios (e.g., driver license
photos) to unconstrained and uncontrolled scenarios (e.g., video surveillance),
new challenges are encountered ranging from illumination, image resolution, and
background clutter to facial pose, expression, and occlusion. In forensic
investigations where the goal is to identify a "person of interest"
based on low quality evidence, we need to utilize whatever information is
available about the suspect. This could include one or more video sequences,
multiple still images captured by bystanders, and descriptions of the suspect
provided by witnesses. The description of the suspect could lead to drawing of
facial sketch and provide some ancillary information about the suspect (age,
gender, race, scars, marks, and tattoos). While traditional face matching
methods take single media (a still face image, video track, or face sketch) as
input, our research considers the entire media collection as a probe or query
to generate a single candidate list for the person of interest. We show that
our approach boosts the likelihood of forensic identification through the use
of different fusion schemes, three-dimensional face models, and incorporation
of quality measures for fusion and video frame selection.

This work was supported in
part by National Physical Science Consortium Graduate
Fellowship

Multi-Kernel Multi-Label Ranking

Authors: Serhat S. Bucak; Anil K. Jain

Abstract:Recent studies have shown that
multiple kernel learning is very effective for image classification, leading to
the popularity of kernel learning in computer vision problems. In this work, we
formulate image classification as a multi-label learning problem and develop an
efficient algorithm for multi-label multiple kernel learning (ML-MKL). We assume
that all the classes under consideration share the same combination of kernel
functions, and the objective is to find the optimal kernel combination that
benefits all the classes. In addition, we address multi-label learning with
many classes via a ranking approach, termed multi-label ranking. Given a test
image, the proposed scheme aims to order all the object classes such that the
relevant classes are ranked higher than the irrelevant ones. We propose a
wrapper approach that learns the ranking functions and optimal linear
combination of base kernels simultaneously. Our experiments on ESP Game and
MIRFlickr image datasets demonstrate the superior performance of the proposed
multi-kernel multi-label ranking approach for image classification.

Abstract:Facial makeup has the ability to alter
the appearance of a person. Such an alteration can degrade the accuracy of
automated face recognition systems, as well as that of methods estimating age
and beauty from faces. In this work, we design a method to automatically detect
the presence of makeup in face images. The proposed algorithm extracts a
feature vector that captures the shape, texture and color characteristics of
the input face, and employs a classifier to determine the presence or absence
of makeup. Besides extracting features from the entire face, the algorithm also
considers portions of the face pertaining to the left eye, right eye, and
mouth. Experiments on two datasets consisting of 151 subjects (600 images) and
125 subjects (154 images), respectively, suggest that makeup detection rates of
up to 93.5% (at a false positive rate of 1%) can be obtained using the proposed
approach. Further, an adaptive pre-processing scheme that exploits knowledge of
the presence or absence of facial makeup to improve the matching accuracy of a
face matcher is presented.

This work was supported in
part by the NSF Center for
Identification Technology Research (CITeR).

Identifying Transcription Start Sites And Transcription End Sites Of MiRNAs
In C.elegans

Authors: Jiao Chen; Yanni Sun

Abstract:MiRNAs are crucial small non-coding
RNAs that regulate gene expression in the growth period of C.elegans.
Transcriptional regulation of miRNAs is critical because it directly affects
miRNA-mediated gene regulatory networks. However, the transcription start sites
(TSSs) and transcription termination sites (TTSs) of most miRNA genes have not
been characterized because pri-miRNAs are quickly spliced in cells. Here, we
performed a whole genome analysis of DNA sequence, chromatin signatures, and Polymerase
II surrounding intergenic miRNAs in C.elegans genome to identify their TSSs and
TTSs. Our results will improve the understanding of the regulation of miRNAs.

Visual Diagram Interpretation For Blind Programmers

Authors: Sarah Coburn; Charles Owen

Abstract:Computer Science education frequently
demonstrate program structure through the use of visual diagrams (such as UML
diagrams), which are largely inaccessible to blind programmers. These diagrams
show things like relationships between objects in the program using lines to
connect shapes, with types of shapes indicating the types of objects and
relationships. This heavy reliance on visual cues (such as peripheral
information and complicated connections between objects) is a hurdle that blind
programmers must get over in order to succeed academically. Some programs exist
to translate UML diagrams into a format readable by blind programmers, but
frequently do not accurately and efficiently communicate all of the essential
information. We developed a program that will automatically interpret UML
diagrams into an auditory format. Information is related using a combination of
audio tones and text to speech audio presented in stereo to help relate
location. We will also discuss information that should be included by any
diagram translator, and the future directions of this research.

A Difference Resolution Approach To Compressing Access Control Lists

Authors: James Daly; Alex X. Liu; Eric Torng

Abstract:Access Control Lists (ACLs) are the
core of many networking and security devices. As new threats and
vulnerabilities emerge, ACLs on routers and firewalls are getting larger.
Therefore, compressing ACLs is an important problem. We present a new approach,
called Diplomat, to ACL compression. The key idea is to transform higher
dimensional target patterns into lower dimensional patterns by dividing the
original pattern into a series of hyperplanes and then resolving differences
between two adjacent hyperplanes by adding rules that specify the differences.
This approach is fundamentally different from prior ACL compression algorithms
and is shown to be very effective. We implemented Diplomat and conducted
side-by-side comparison with the prior Firewall Compressor, TCAM Razor and ACL
Compressor algorithms on real life classifiers. Our experimental results show
that Diplomat outperforms all of them on most of our real-life classifiers,
often by a considerable margin, particularly as classifier size and complexity
increases. In particular, on our largest ACLs, Diplomat has an average
improvement ratio of 30.6% over Firewall Compressor on range-ACLs, of 12.1%
over TCAM Razor on prefix-ACLs, and 9.4% over ACL Compressor on mixed-ACLs.

This work was supported in
part by Nation Science Foundation Grant No. CNS-0916044

iSleep: Unobtrusive Sleep Quality Monitoring Using Smartphones

Authors: Tian Hao; Guoliang Xing; Gang Zhou

Abstract:The quality of sleep is an important
factor in maintaining a healthy life style. To date, technology has not enabled
personalized, in-place sleep quality monitoring and analysis. Current sleep
monitoring systems are often diffcult to use and hence limited to sleep
clinics, or invasive to users, e.g., requiring users to wear a device during
sleep.

iSleep is a practical system
to monitor an individual's sleep quality using off-the-shelf smartphone. It
uses the built-in microphone of the smartphone to detect the events that are
closely related to sleep quality, including body movement, cough and snore, and
infers quantitative measures of sleep quality. By providing a fine-grained
sleep profile that depicts details of sleep-related events, iSleep allows the
user to track the sleep efficiency over time and relate irregular sleep
patterns to possible causes.

This work was supported in
part by This work is supported in part by the NSF under grant
CNS-0954039 (CAREER), CNS-1250180 and ECCS-0901437.

Abstract:Advancements in wireless communication
techniques have increased the wireless physical layer (PHY) data rates by
hundreds of times in a dozen years. The high PHY data rates, however, have not
been translated to commensurate throughput gains due to overheads incurred by
medium access control (MAC) and PHY convergence procedure. At high PHY data
rates, the time used for collision avoidance (CA) at MAC layer and the time
used for PHY convergence procedure can easily exceed the time used for
transmission of an actual data frame. As collision detection (CD) in wireless
communication became feasible recently, some protocols migrate random backoff
from the time domain to the frequency domain, but they fail to address the
introduced high collision probability. We investigate the practical issues of
CD in the frequency domain and introduce a binary mapping scheme to reduce the
collision probability. Based on the binary mapping, a bitwise arbitration (BA)
mechanism is devised to grant only one transmitter the permission to initiate
data transmission in a contention. With the low collision probability achieved
in a short bounded arbitration phase, the throughput is significantly improved.
Because collisions are unlikely to happen, unfairness caused by capture effect
of radios is also reduced. The bitwise arbitration mechanism can further be set
to let high priority messages get through unimpeded, making WiFi-BA suitable
for real time prioritized communication. We validate the effectiveness of
WiFi-BA through implementation on FPGA of USRP E110. Performance evaluation
demonstrates that WiFi-BA is more efficient than current Wi-Fi solutions.

Abstract:IEEE 802.22 is the first standard
based on the concept of cognitive radio. It recommends collaborative spectrum
sensing to avoid the unreliability of individual spectrum sensing while
detecting primary user signals. However, it opens an opportunity for attackers
to exploit the decision making process by sending false reports. In this paper,
we address security issues

regarding distributed node
sensing in the 802.22 standard and discuss how attackers can modify or
manipulate their sensing result independently or collaboratively. This problem
is commonly known as spectrum sensing data falsification (SSDF) attack or
Byzantine attack. To counter the different attacking strategies, we propose a
reputation based clustering algorithm that does not require prior knowledge of
attacker distribution or complete identification of malicious users.We provide
an extensive probabilistic analysis of the performance of the algorithm. We
compare the performance of our algorithm against existing approaches across a wide range of attacking
scenarios. Our proposed algorithm displays a significantly reduced error rate
in decision making in comparison to current methods. It also identifies a large
portion of the attacking nodes and greatly minimizes the false detection rate
of honest nodes.

A Wireless Sensor Network Within An Aquatic Environment

Authors: Tam Le; Matt Mutka

Abstract:Wireless sensor networks have been
widely used in many environmental monitoring applications. For aquatic
environments, the deployment is quite expensive since the sensors need to be
anchored to prevent them from floating away and losing communications. We
propose an inexpensive and flexible approach to provide environmental
monitoring in aquatic environments. We propose a special mobile sensor robot
that acts as a mobile base station and travels the water area to collect data
from sensors as well as locations that cannot be covered by sensors. The
sensors in the water have a jumping capability that enables an extended
communication range in comparison to sensors that merely float upon the water.
By leveraging the jumping capability, the sensors can collaborate with others
to exchange data and communicate with the robot, so that the robot can compute
an efficient path to travel. The problems we study are: 1) given a set of visited
points, how to find the robot’s optimal path with support of sensors to cover
the remaining points; 2) to design an efficient jumping strategy and
communication protocol between sensors.

Abstract:Plant microRNA prediction tools that
utilize small RNA sequencing data are emerging with the advances of the next
generation sequencing technology. These existing tools have at least one of the
following problems: 1. high false positive rate; 2. the positions of the predicted
miRNAs are not accurate; 3. long running time; 4. work only for genomes in
their databases; 5. hard to install or use. We develop miR-PREFeR, which
utilizes expression patterns of miRNA and follows the criteria for plant
microRNA annotation to accurately predict plant miRNAs from one or more small
RNA-Seq data samples of the same species. We tested miR-PREFeR on several plant
species. The results show that miR-PREFeR is sensitivity, accurate, fast, and
has low memory footprint.

This work was supported in
part by NSF

Discrete Connection And Covariant Derivative For Vector Field Analysis And
Design

Authors: Beibei Liu; Fernando de Goes; Yiying Tong; Mathieu Desbrun

Abstract:In this paper, we introduce a discrete
deﬁnition of connection on simplicial manifolds, with closed-form continuous
expressions within simplices and ﬁnite
rotations across simplices. The ﬁnite-dimensional parameters of
this connection are optimally generated by minimizing a quadratic
measure of the deviation to the discontinuous connection induced by
the embedding of the input mesh. We also construct from this
discrete connection a covariant derivative through exact differentiation,
leading to analytical expressions for local integrals of
ﬁrst-order derivatives (such as divergence, curl and the Cauchy-Riemann
operator), and for L2-based energies (such as the Dirichlet
energy). We ﬁnally demonstrate the utility, ﬂexibility, and accuracy of
our discrete formulations for the design and analysis of vector,
n-vector, and n-direction ﬁelds.

Abstract:To support natural interaction between
a human and a robot, technology enabling human-robot dialogue has become
increasingly important. In human-robot dialogue, although a robot and its human
partner are co-present in a shared environment, they have significantly
mismatched perceptual capabilities (e.g., recognizing objects in the
surroundings). When a shared perceptual basis is missing, communication about
the shared environment often becomes difficult, such as identifying referents
in the physical world that are referred to by the human (i.e., a problem of
referential grounding). To overcome this challenging problem, we have developed
an optimization based approach that allows the robot to quickly adapt to the
perceptual differences. Given any new situation, through a couple of dialogues,
the robot can quickly learn a set of weights indicating how reliable/unreliable
each dimension of its perception of the environment maps to human’s linguistic
expressions. The robot then adapts to the situation by applying the learned
weights for grounding linguistic expressions to physical entities. Our
empirical results have shown that, when the perceptual difference is high
(i.e., the robot can only correctly recognize 10-40% of objects in the
environ-ment), applying learned weights significantly improves referential
grounding performance by an absolute gain of 10%.

This work was supported in
part by N00014-11-1-0410 from the
Office of Naval Research and IIS-1208390 from the National Science Foundation.

Abstract:Duty cycling improves energy
efficiency but lim- its throughput and introduces significant end-to-end delay
in wireless sensor networks. In this paper, we present a traffic- adaptive
synchronous MAC protocol (TAS-MAC), which is a high throughput low delay MAC
protocol tailored for low power consumption. It achieves high throughput by
using Time Division Multiple Access (TDMA) with a novel traffic-adaptive
allocation mechanism that assigns time slots only to nodes located on active
routes. TAS-MAC reduces the end-to-end delay by notifying all nodes on active
routes of incoming traffic in advance. These nodes will claim time slots for
data transmission and forward a packet through multiple hops in a cycle. The
desirable traffic-adaptive feature is achieved by decomposing traffic
notification and data transmission scheduling into two phases, specializing
their duties and improving their efficiency respectively. Simulation results and
tests on TelosB motes demonstrate that the two-phase design significantly
improves the throughput of current synchronous MAC protocols and achieves the
similar low delay of slot stealing assisted TDMA with much lower power
consumption.

Abstract:The proliferation of smartphones in
recent years has led to a phenomenal growth in the number and variety of mobile
applications developed for personal use, businesses, education, and other
purposes. The app markets, such as Google Play and Apple iTunes, provide a
one-stop shop for users to download or purchase their apps and for software
developers to market their inventions. As the number of mobile apps rapidly
grows, searching or recommending relevant apps for users becomes a challenging
problem. The broad, coarse-grained categories currently provided by the market
place may not fit the actual description and intended use of the apps. In this
poster, we present a hierarchical classification approach based on non-negative
matrix tri-factorization to classify mobile apps while simultaneously
constructing a category tree that reveals a deeper relationship among the
categories. We demonstrate the limitations of using existing concept
hierarchies (such as Google Ad Trees) and present a semi-supervised learning
approach that integrates existing hierarchies with the mobile app description
data to significantly improve classification accuracy.

Toward Tractable Instantiation Of Conceptual Data Models

Authors: Matthew Nizol; Laura K. Dillon; R.E.K. Stirewalt

Abstract:Complex, data-intensive software
systems play an increasingly crucial role in enterprise decision making.
Developers of these systems must validate both the database design and the application
programs that interact with the database. If a conceptual data model is
developed during requirements analysis, instantiation of that model can
facilitate both validation activities.

Domain experts can inspect
test instances of the model to confirm that constraints have been properly
expressed, and application programmers can use generated data to test their
programs. Object Role Modeling (ORM) is a popular modeling language that maps
to predicate logic. Due to ORM's expressive constraint language, instantiating
an arbitrary ORM model is NP-hard, but a restricted subset of the language
called ORM- can be solved in polynomial time. Some models that include
"hard" constraints (i.e., constraints outside the ORM- subset) can
nevertheless be transformed into ORM- models. Such transformations do not
necessarily need to preserve the original model's semantics: the existence of
some mapping from instances of the target model to instances of the original
model is sufficient. This poster presents a research project to extend the set
of ORM models that can be transformed to ORM- models through a class of
non-semantics-preserving transformations called constraint strengthening. We
illustrate an example constraint-strengthening transformation and note
limitations of the approach.

Future research will
investigate the composition of transformations, the use of genetic algorithms
to search for instances of complex models, and the use of SAT-solvers to find
partial instances of the "hard" portions of a model that may be
combined to form an instance of the original model.

Regular Distance-Preserving Graphs

Authors: Ronald Nussbaum; Abdol-Hossein Esfahanian

Abstract:A graph is distance-hereditary if the
distances in any connected induced subgraph are the same as those in the
original graph. Relaxing the requirement that every connected induced subgraph
be distance-preserving allows us to explore the idea of a distance-preserving
graph. Formally, a graph of order n is distance-preserving if for each integer
k in the interval [1, n] there exists at least one isometric subgraph of order
k. Previously we worked to characterize and find applications for distance-preserving
graphs. Here we give methods for constructing r-regular distance-preserving
preserving graphs on n vertices for various values of r and n. We also consider
constructing r-regular non-distance-preserving graphs on n vertices for various
values of r and n, and related conjectures.

De-Identifying Biometric Images For Enhancing Privacy And Security

Authors: Asem Othman; Arun Ross

Abstract:The goal of this poster is to discuss
methods that have been developed in our lab (i-probe) to extend privacy to
biometric data in the context of an operational system. Biometric data can be
viewed as personal data, since it pertains to the biological and behavioral
attributes of an individual. Therefore, it is necessary to ensure that the
biometric data stored in a system is used only for its intended purpose by
de-identifying prior to storage. In this poster, we will briefly discuss two
approaches to de-identify biometric images. The first approach is based on
Visual Cryptography that de-identifies a face image prior to storing it by
decomposing the original image into two images in such a way that the original
image can be revealed only when both images are simultaneously available;
further, the individual component images do not reveal any information about
the original face image. The second approach is based on the concept of mixing
to extend privacy to fingerprint images. The proposed scheme mixes a
fingerprint with another fingerprint (referred to as the "key") in
order to generate a new mixed fingerprint image that can be directly used by a
fingerprint matcher. The mixed image obscures the identity of the original
fingerprint; further, different applications can employ different
"keys", thereby ensuring that the identities enrolled in one
application cannot be matched against the identities in another application.

Abstract:We present a generic framework for
automatic age, gender and race estimation from face images, including a quality
assessment measure used to identify low-quality images for which it will be
difﬁcult to obtain reliable estimates. Experimental results on a diverse set of
face image databases show that the proposed approach has better performance
than other state of the art methods. Finally, we use crowdsourcing to study
humans’ ability to estimate demographics from face images, and compare the
crowdsourced estimates to our automatic demographic estimates.

RAIL: Robot-Assisted Indoor Localization

Authors: Chen Qiu; Matt Mutka

Abstract:Location Based Services (LBS) are
expanding rapidly for mobile devices. Global Positioning System (GPS) has been
commonly adopted for outdoor localization. However, since the accuracy of GPS
is very low (or nonexistent) indoors, it cannot support LBS in indoor
environments. The indoor location information available for most current mobile
devices is not accurate. We introduce an approach that improves a smartphone's
localization accuracy with help of a moving robot. By installing on a robot a
tablet personal computer, the proposed application program and a known map,
moving robots can improve a smartphone's localization accuracy. The robot can
use Bluetooth to send its accurate location information to the customers'
smartphones. Customers who carry smartphones do not need any special-purpose
device to obtain location information. We need to design a path for a robot so
that all the smartphones in the environment may have smaller deviations from
the ground truth due to interaction with the robot. The robot collects
Bluetooth RSSI values from smartphones in different rooms. We classify
different rooms into different crowd density levels by the RSSI values. Higher
crowd density rooms should be served more often. By using different crowd
density levels, we use dynamic programming to design algorithms to generate a
robot's moving route. We evaluate our approach in different environments, the
location errors from a localization application on a smartphone are reduced
effectively. After each serving round, a robot can choose an appropriate
algorithm from proposed algorithms according to crowd density.

This work was supported in
part by the National
Science Foundation grant no. CNS-1320561.

Efficient Kernel-Based Data Stream Clustering

Authors: Radha Chitta; Anil K. Jain

Abstract:Recent advances in sensor technologies
have facilitated “continuous” data collection. Unbounded sequences of data
called data streams are generated in many applications such as IP networks,
stock markets, and social networks. There are two major challenges in data
stream analysis: (i) Due to the unbounded nature of the data, it is not
possible to store all the data in memory, so the data can be accessed at most
once, and (ii) the data evolves over time, i.e. the recent data in the stream
may be unrelated to the older data in the stream.

Stream clustering is the task
of finding groups in the data stream, based on a pre-defined similarity
measure. Most of the current stream clustering algorithms are “linear”
clustering algorithms, and use Euclidean similarity. Kernel-based clustering
algorithms use non-linear similarity measures, thereby achieving higher
clustering accuracy than linear clustering algorithms. However, kernel-based
clustering algorithms are ill-suited to streams because of their high
computational complexity. In this poster, we present an approximate
kernel-based stream clustering technique which identifies the most influential
points in the stream, and retains only these points in memory. The final
clusters are then obtained using only the stored data points. Only a small
subset of the data (less than 1%) needs to be stored in memory, thereby
enhancing the efficiency of kernel clustering for data streams. We demonstrate
the accuracy and efficiency of our approximate stream clustering algorithm on
several public domain data sets like the Network Intrusion and Tiny image data
sets.

This work was supported in
part by the Oﬃce of Naval Research (ONR
Grant N00014-11-1-0100).

Detecting Fake Fingerprints

Authors: Ajita Rattani; Arun Ross

Abstract:Recent research has highlighted the
vulnerability of fingerprint recognition system to spoof attacks. A spoof
attack occurs when an adversary mimics the fingerprint of another individual in
order to circumvent the system. Fingerprint liveness detection algorithms have
been used to disambiguate live fingerprint samples from spoof (fake)
fingerprints fabricated using materials such as latex, gelatine, etc. Most
liveness detection algorithms are learning based and dependent on the a)
fabrication material used to generate and b) sensor used to acquire the fake
fingerprints during the training stage. Consequently, the performance of a
liveness detector is significantly degraded in multi-sensor environment and
when novel fabrication materials are encountered during the testing stage. The
aim of this work is to improve the interoperability of fingerprint liveness
detectors across different sensors and fabrication materials. To this aim, the
contributions of this work are i) a graphical model that accounts for the
impact of the sensor on fingerprint match scores, quality and liveness measures
and ii) a pre-processing scheme to reduce the impact of fabrication material on
fingerprint liveness detector.

Local Predictions In Social Network Graphs

Authors: Dennis Ross; Guoliang Xing; Abdol-Hossein Esfahanian

Abstract:Using the data from social networks,
predictions have been made in several domains including: disease proliferation
modeling, criminal activity detection, and recommender system design. With data from
established social networks, like Twitter and Facebook, we try to make accurate predictions of
several national trends on a local level. To do this with a social network G, an influential
subgraph is created for each vertex v called Gamma of v . Each Gamma of v is
chosen using a variety of graph
properties like degree, modularity, and the clustering coefficient. Efficient algorithms to
determine Gamma of v are discussed. By extracting the influential subgraph for each v in G, we attempt
to make relevant predictions for any individual user. Some results will be presented
along with potential real-world system deployments.

On Hair Recognition In The Wild By Machine

Authors: Joseph Roth; Xiaoming Liu

Abstract:We present an algorithm for identity
verification using only the information from the hair. Face recognition in the
wild (i.e., unconstrained settings) is highly useful in a variety of
applications, but performance suffers due to many factors, e.g., obscured face,
lighting variation, extreme pose angle, and expression. It is well known that
humans utilize hair for identification under many of these scenarios due to
either the consistent hair appearance of the same subject or obvious hair
discrepancy of different subjects, but little work exists to replicate this
intelligence artificially. We propose a learned hair matcher using shape,
color, and texture features derived from localized patches through an AdaBoost
technique with abstaining weak classifiers when features are not present in the
given location. The proposed hair matcher achieves 71.53% accuracy on the LFW
View 2 dataset. Hair also reduces the error of a COTS face matcher through
simple score-level fusion by 5.7%.

NSGA-III Performance In Bi-Objective Optimization

Authors: Haitham Seada; Kalyanmoy Deb

Abstract:NSGA-III is a recently suggested
evolutionary many-objective optimization algorithm that is designed to solve
three or more objective problems. Although, NSGA-III was found to be superior
to other state of the art algorithms in handling three or more objectives (up
to 20), no formal assessment of its performance was conducted on handling only
two objectives. This study aims at directing subsequent lines of research,
either towards enhancing NSGA-III in terms of two objectives without
sacrificing its superiority in higher number of objectives, or towards a more
unified version of NSGA-III that can handle any arbitrary number of objectives
with the same efficiency. In this paper, we assess the performance of NSGA-III
against a number of test as well as real-life engineering problems. We also
empirically investigate the effect of some critical parameters on the overall
performance. Based on the obtained results, we introduce some interesting
directions that researchers in the field can pursue in the future.

Secure Unlocking Of Mobile Touch Screen Devices By Simple Gestures: You
Can See It But You Can Not Do It

Authors: Muhammad Shahzad; Alex X. Liu; Arjmand Samuel

Abstract:With the rich functionalities and
enhanced computing capabilities available on mobile computing devices with
touch screens, users not only store sensitive information (such as credit card
numbers) but also use privacy sensitive applications (such as online banking)
on these devices, which make them hot targets for hackers and thieves. To
protect private information, such devices typically lock themselves after a few
minutes of inactivity and prompt a password/PIN/pattern screen when
reactivated. Passwords/PINs/patterns based schemes are inherently vulnerable to
shoulder surfing attacks and smudge attacks. Furthermore, passwords/PINs/patterns
are inconvenient for users to enter frequently. We propose GEAT, a gesture
based user authentication scheme for the secure unlocking of touch screen
devices. Unlike existing authentication schemes for touch screen devices, which
use what user inputs as the authentication secret, GEAT authenticates users
mainly based on how they input, using distinguishing features such as finger
velocity, device acceleration, and stroke time. Even if attackers see what
gesture a user performs, they cannot reproduce the behavior of the user doing
gestures through shoulder surfing or smudge attacks. We implemented GEAT on
Samsung Focus running Windows, collected 15009 gesture samples from 50
volunteers, and conducted real-world experiments to evaluate GEAT's performance.
Experimental results show that our scheme achieves an average equal error rate
of 0.5% with 3 gestures using only 25 training samples.

Assembly In The Cloud: Benchmarking

Authors: Leigh Sheneman; C. Titus Brown

Abstract:The project focuses on quantifying the
computational effectiveness of mRNAseq protocols on various cloud computing
platforms. The Illumina HiSeq 2500 (currently used at MSU's RTFS) can produce
600 GB of data per run. While the price of extracting a single dataset at this
sensitivity level is extremely high in its own right, adding in steps to
assemble and analysis it can easily cost $10K. This results in small biology labs
being instantly excluded from conducting research.

Through high-level analysis
of patterns in during all stages of the protocol, bottlenecks in algorithms can
be identified and addressed. Since each cloud-computing cluster has a different
hardware implementation, the bottleneck is not universally constant. By
leveraging the strengths of each platform, the GED lab aims to reduce overall
cost.

The Eel Pond mRNAseq Tutorial
by C. Titus Brown, et al., has been the basis of initial testing. The results
from this testing show Amazon's vCPU system out-performs traditional CPU
structures.

Who Will Go Viral? A Distribution-Preserving Approach For Node Degree
Prediction In Social Networks

Abstract:Predicting the future degree of a new
node in an evolving network is an important problem, with many potential
applications. For example, advertisers may want to know who may become the next
most popular or influential user in a social network. Similarly, predicting
highly retweeted tweets or highly liked social media could help detect breaking
news or postings that may go viral. In this study, we consider two approaches
for node degree prediction. Node degrees can be predicted directly, e.g., using
regression-based approach, or indirectly, e.g., using link prediction to infer
the presence or absence of the links associated with a given node. Though
regression methods are more accurate than link prediction, their predicted
degree distribution may not fully satisfy the power law distribution typically
observed in many real-world networks. In this poster, we present a
distribution-regularized regression framework to predict the future degree of the
nodes in a network using both the node feature and link information.
Experimental results on real-world networks demonstrate both the accuracy of
the prediction as well as fidelity of the predicted distribution compared to
other baseline methods.

Stain Simulation On Curved Surfaces Through Homogenization

Authors: Shiguang Liu; Xiaojun Wang; Yiying Tong

Abstract:This poster provides methods for
physically-based simulation in stain formation in computer graphics. We propose
to use proper averaging of the textile diffusion property to create realistic
stains. The simulation is performed directly on the surface. For different type
of knitting, we apply the homogenization technique in 2D to extract bulk
diffusion tensor which is anisotropic in general. We then map the diffusion
tensor onto curved surfaces by specifying the alignment of the textile to a
direction field on the surface. The influence on the shape of the stain is
determined by using the inertial force experienced in a comoving framework
attached to the deforming surface. Our results demonstrate that the process is
physically plausible.

Abstract:Ensemble forecasting is a well-known
numerical prediction technique for modeling nonlinear dynamic systems. The
ensemble member forecasts are generated from computer-simulated models, where
each forecast is obtained by perturbing the initial conditions or using a
different model representation

of the dynamic system. The
ensemble mean or median is typically chosen as a point estimate of the final
forecast for decision making purposes. However, this approach is limited in
that it assumes each ensemble member is equally skillful and does not consider
the inherent correlations that may exist among the ensemble members. In this
poster, we cast the ensemble forecasting task as an online, multi-task
regression problem with partially observed data and present a novel framework
called ORION to estimate the optimal weighted combination of the ensemble
members. The weights are updated using an online learning with restart
algorithm to deal with the partially observed data.

The framework can accommodate
different types of loss functions including epsilon-insensitive and quantile
loss. Experimental results on seasonal soil moisture predictions from 12 major
river basins in North America demonstrate the superiority of the proposed
approach compared to the ensemble median and other baseline methods.

This work was supported in
part by NOAA Climate Program
office through grant NA12OAR4310081 and partially supported by NASA Terrestrial
Hydrology Program through grant NNX13AI44G.

Abstract:The scale of the plant phenotyping
data is growing exponentially, and they have become a first-class asset for
understanding the mechanisms affecting energy intake and storage in plants,
which are essential for improving crop productivity and biomass. However, the
quality of data is compromised by systematic errors, unbiased noise as well as
abnormal patterns, which are difficult to remove in data collection step. Given
the value of clean data for any operation, the ability to improve their quality
is a key requirement.

Data cleaning is the process
of identifying incorrect or corrupt records in a dataset, integrating ad-hoc
tools, manually tuned algorithms designed for specific tasks, and ideal
statistical methods. However, removing impurities from long time-series plant
phenotyping data requires the handling of high temporal dimension, which has
not been extensively discussed in literature.

In this work, we develop a
novel computational framework to effectively identify abnormalities in plant
phenotyping data using Michaelis-Menten kinetics, one of the best-known models
of enzyme kinetics in biochemistry. Specifically, our model employs an EM
process to repeatedly classify the temporal data into two classes:
abnormalities and non-abnormalities. In each iteration, it uses values of
non-abnormality class to generate photosynthesis-irradiance curves at different
granularities using Michaelis-Menten kinetics, and then reassigns the class
membership of every value based on their fitness to the curves. The iteration
stops when all the class memberships don't change. The results show our
algorithm can identify most of the abnormalities in both real and synthetic
datasets. Note that our algorithm is independent of actual biological
constrains. With simple extension, it makes it possible to automate the
cleansing process on long time-series data for a variety of domains.

Abstract:Fingerprint recognition is widely used
to identify a person in applications ranging from law enforcement and
international border control to mobile phone access. Friction ridge patterns,
including fingerprints and palmprints, have been one of the major sources of
evidence in crime scene investigation. There are two properties of friction
ridge patterns invoked in promoting its use: (i) persistence (ridge pattern
does not change over time), and (ii) uniqueness (ridge pattern of a finger is
different from any other finger). The admissibility of fingerprints as evidence
was accepted in Frye v. United States in 1923. However, the general acceptance
test of Frye ruling was superseded by the Federal Rules of Evidence in Daubert
v. Merrell Dow Pharm. in 1993. Since then, friction ridge analysis has been
challenged on the basis of the fundamental premises, persistence and
uniqueness. Although a number of statistical models have been proposed to
demonstrate fingerprint uniqueness, the persistence of fingerprints has
generally been accepted based on anecdotal evidence. In this study, our
objectives are to (i) formally study the impact of elapsed time between two
fingerprint impressions on genuine match (comparing multiple impressions of the
same finger) scores, (ii) model the fingerprint longitudinal data with
multilevel statistical models, (iii) identify additional predictive variables
of genuine match scores (e.g., subject’s age, gender, race, etc.), and (iv)
quantify the impact of these factors on genuine match scores. Our preliminary
study shows that the genuine match scores decrease with respect to elapsed
time, but with a very small rate-of-change.

This work was supported in
part by NSF's Center for Identification Technology Research
(CITeR)

Abstract:Are your visual capabilities largely
learned or largely innate?How does a
human child learn directly from his cluttered environments?How do his actions play a central role in
not only associating between sensation with the required action, but also
attention and perception of objects of interest in a cluttered scene?How does the child develop concepts and
invariant properties when he is not even aware of such concepts?Our research group has been addressing these
scientific questions that are fundamental to not only Artificial Intelligence
(AI) and its practical applications but also our understanding of human
intelligence.This poster explains the
work after Where-What Network 8 (WWN-8), where we intend to show how the above
questions are addressed not only in a brain-inspired way, but also in terms of
efficiency of autonomous learning:How
learning must incorporate various modes of learning by a single general-purpose
architecture --- supervised learning, coarse-to-fine learning, and
reinforcement learning (i.e., learning through punishments and rewards).